class_docstr: Creates a Laplace distribution parameterized by :attr:`loc` and :attr:`scale`.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
      >>> m.sample()  # Laplace distributed with loc=0, scale=1
      tensor([ 0.1046])
  
  Args:
      loc (float or Tensor): mean of the distribution
      scale (float or Tensor): scale of the distribution
class_docstr_tok: ["Creates","a","Laplace","distribution","parameterized","by",":","attr",":","`","loc","`","and",":","attr",":","`","scale","`",".","Example",":",":",">",">",">","#","xdoctest",":","+IGNORE_WANT","(","``","non-deterministic","''",")",">",">",">","m","=","Laplace","(","torch.tensor","(","[","0.0","]",")",",","torch.tensor","(","[","1.0","]",")",")",">",">",">","m.sample","(",")","#","Laplace","distributed","with","loc=0",",","scale=1","tensor","(","[","0.1046","]",")","Args",":","loc","(","float","or","Tensor",")",":","mean","of","the","distribution","scale","(","float","or","Tensor",")",":","scale","of","the","distribution"]
code: def expand(self, batch_shape, _instance=None):
      new = self._get_checked_instance(Laplace, _instance)
      batch_shape = torch.Size(batch_shape)
      new.loc = self.loc.expand(batch_shape)
      new.scale = self.scale.expand(batch_shape)
      super(Laplace, new).__init__(batch_shape, validate_args=False)
      new._validate_args = self._validate_args
      return new
  
file_path: pytorch/torch/distributions/laplace.py
imports: from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 6 for calling the Laplace obj's underlying member method code with example usage: obj.expand(batch_shape, _instance) and returns: new